Various imaging systems and tools have been developed to assist physicians, clinicians, radiologists, etc. in evaluating medical images to diagnose medical conditions. For example, computer-aided detection (CAD) tools have been developed for various clinical applications to provide automated detection of medical conditions in medical images, such as colonic polyps and other abnormal anatomical structures such as lung nodules, lesions, aneurysms, calcification, in breast, heart or artery tissue, etc.
Magnetic resonance imaging (MRI) is a medical imaging technique that uses a powerful magnetic field to image the internal structure and certain functionality of a body. MRI is particularly suited for imaging soft tissue structures and is thus highly useful in the field of oncology for the detection of lesions.
Dynamic contrast-enhanced MRI (DCE-MRI) allows for many additional details pertaining to bodily soft tissue to be observed, to further aid in diagnosis and treatment of detected lesions. DCE-MRI may be performed by acquiring a sequence of magnetic resonance (MR) images that span a time before magnetic contrast agents are introduced into the patient's body and a time after the magnetic contrast agents are introduced. By imaging the patient's body sequentially, a set of images may be acquired that illustrate how the magnetic contrast agent is absorbed and washed out from various portions of the patient's body.
This wash-in (i.e. absorption) and wash-out information may be used to characterize various internal structures within the body and provide additional diagnostic information. In malignant lesions, for example, the amount of contrast agent in the tissue increases and then decreases over time, indicated by a wash-in followed by a wash-out enhancement pattern. The enhancement pattern may then be fitted to a pharmacokinetic model from which parameters based on the rates of fluid exchange can be calculated. One example of a pharmacokinetic model is the Tofts model, which fits a model that consists of a linear increase of contrast agent in the blood and an exponential decay representing the leak from the lesion, to model the flow and leakage from the vessel. See P. S. Tofts, G Brix, D. L. Buckley, J. L. Evelhoch, E. Henderson, M. V. Knopp, H. B. W. Larsson, T. Y. Lee, N. A. Mayr, G J. M. Parker, et al., “Estimating kinetic parameters from dynamic contrast-enhanced T I-weighted MRI of diffusible tracer: a common global language for standardized quantities and symbols,” J. Magn. Reson. Imaging, 10(3):223-232, 1999.
The diagnosis of cancer from MRI data is a difficult problem. A malignant lesion often displays intensity patterns similar to benign tissues and other structures in the field of view. Additional difficulties are posed when an enhancement pattern is associated with a lesion, because a lesion comprises a large number of voxels (i.e. points), each voxel showing a different enhancement pattern. Several methods have been proposed to characterize the enhancement pattern of lesions. However, such conventional methods do not take into account the motion of the contrast agent inside the lesion. For example, a centrifugal motion of enhancement is often seen inside a fibroadenoma that is benign. Conventional methods will produce a wash-in/wash-out enhancement pattern that makes the lesion look malignant, when in fact it is benign. Therefore, there is a need for a new technology that accurately characterizes enhancement patterns to avoid such false positives.
In addition, there is also a need to address the computationally intensive aspects of pharmacokinetic analysis (PKA). PKA typically involves a large number of powerful but computationally heavy features. For example, the Tofts Model described above fits the enhancement pattern to the flow model by iteratively minimizing the difference between the enhancement data and the exponential curve with the current decay parameter. Such an iterative process, as well as other aspects of the analysis, demand tremendous computational resources. Therefore, there is a need for a technology that increases the computational efficiency of CAD systems.